Primary tabs

PET/CT Signatures to Optimize Tuberculosis Host-Directed Therapy (HDT) Development

Yingda Xie of Rutgers, The State University of NJ and JoAnne Flynn of the University of Pittsburgh, both in the U.S., will develop a non-invasive approach for testing candidate anti-tuberculosis compounds in animal models and patients using positron emission tomography-x-ray computed tomography (PET/CT). Tuberculosis (TB) is a leading cause of death in developing countries, and rates are sustained by the causative bacterium, Mycobacterium tuberculosis, developing resistance to current drugs. To circumvent this, new drugs are being designed to target human cells and proteins rather than those of the bacteria. To test these drugs, new tools are also needed to monitor TB in patients. 18 Fluorodeoxyglucose (FDG)-PET/CT is a non-invasive imaging tool that uses radioactively-labelled glucose to light up areas of metabolic activity in the body such as the lesions formed by M. tuberculosis and immune cells that play a critical role in infection. They have histopathological sections and cell and chemical data of TB lesions from non-human primate models and will use them to quantify the different lesions. Then, by using the available PET-CT scans of the lesions, they will search for quantitative signatures that can predict a specific type of lesion. The accuracy of these PET/CT signatures will be tested in a separate group of animals. Their study will reveal details of the TB immune response across different lesions, which could help design new treatments, and the signatures can be used to test the activity of new drug candidates in animal models and humans.

Grant ID
OPP1210947
Show on Hub
On
Show on Spoke
On
Follow-on Funding
Off
Lead Funding Organization
Initiatives
Principal Investigator
Individual Funder Information
Funding Organization
Funding Amount (in original currency)
200000.00
Funding Currency
USD
Funding Amount (in USD)
200000.00
Project Type
Project Primary Sector
Project Subsector
Funding Date Range
-
Funding Total (In US dollars)
200000.00
Co-Funded
False